{"id":"https://openalex.org/W2887941630","doi":"https://doi.org/10.1109/memea.2018.8438719","title":"Convolutional Auto-Encoder Based Deep Feature Learning for Finger-Vein Verification","display_name":"Convolutional Auto-Encoder Based Deep Feature Learning for Finger-Vein Verification","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2887941630","doi":"https://doi.org/10.1109/memea.2018.8438719","mag":"2887941630"},"language":"en","primary_location":{"id":"doi:10.1109/memea.2018.8438719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea.2018.8438719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051669252","display_name":"Borui Hou","orcid":"https://orcid.org/0000-0002-7171-1630"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Borui Hou","raw_affiliation_strings":["School of Instrument Science and Engineering Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011148961","display_name":"Ruqiang Yan","orcid":"https://orcid.org/0000-0002-1250-4084"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruqiang Yan","raw_affiliation_strings":["School of Instrument Science and Engineering Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Instrument Science and Engineering Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5051669252"],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":2.6423,"has_fulltext":false,"cited_by_count":32,"citation_normalized_percentile":{"value":0.91122456,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10828","display_name":"Biometric Identification and Security","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14333","display_name":"Dermatoglyphics and Human Traits","score":0.9750999808311462,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10751","display_name":"Forensic and Genetic Research","score":0.9747999906539917,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7936818599700928},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7539239525794983},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7485801577568054},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7111580967903137},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6835294365882874},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.605507493019104},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5984549522399902},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5574444532394409},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5516963601112366},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5468142628669739},{"id":"https://openalex.org/keywords/convolutional-code","display_name":"Convolutional code","score":0.4758020341396332},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.42741984128952026},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.41619983315467834},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40470272302627563},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.2071913778781891},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10034167766571045}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7936818599700928},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7539239525794983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7485801577568054},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7111580967903137},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6835294365882874},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.605507493019104},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5984549522399902},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5574444532394409},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5516963601112366},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5468142628669739},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.4758020341396332},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.42741984128952026},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.41619983315467834},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40470272302627563},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2071913778781891},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10034167766571045},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/memea.2018.8438719","is_oa":false,"landing_page_url":"https://doi.org/10.1109/memea.2018.8438719","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W561687969","https://openalex.org/W1970972145","https://openalex.org/W2004617923","https://openalex.org/W2039536345","https://openalex.org/W2056634031","https://openalex.org/W2058362416","https://openalex.org/W2078565548","https://openalex.org/W2084609952","https://openalex.org/W2097561865","https://openalex.org/W2102780391","https://openalex.org/W2118323481","https://openalex.org/W2141717716","https://openalex.org/W2490846508","https://openalex.org/W2549667233","https://openalex.org/W2599632008","https://openalex.org/W2626053630","https://openalex.org/W2757352732","https://openalex.org/W2771040504","https://openalex.org/W6739718167","https://openalex.org/W6744386377"],"related_works":["https://openalex.org/W2132373020","https://openalex.org/W2096049278","https://openalex.org/W39762558","https://openalex.org/W2983142544","https://openalex.org/W2891059443","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W4220682630"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,10,54,70,106],"novel":[4],"deep":[5,88],"learning-based":[6],"method":[7,91,100],"that":[8,85],"integrates":[9],"Convolutional":[11,15],"Auto-Encoder":[12],"(CAE)":[13],"with":[14],"Neural":[16],"Network":[17],"(CNN)":[18],"for":[19],"finger":[20,33,43,55,75,113],"vein":[21,34,44,56,76],"verification.":[22],"The":[23,50],"CAE":[24,51],"is":[25,39],"used":[26,40],"to":[27,41],"learn":[28],"the":[29,37,67,86,110],"feature":[30,48,61,80],"codes":[31],"from":[32,45,63,78],"images":[35,77],"and":[36,69],"CNN":[38],"classify":[42],"these":[46],"learned":[47],"codes.":[49],"consists":[52],"of":[53,66,112],"encoder,":[57],"which":[58,72],"extracts":[59],"high-level":[60,79],"representation":[62],"raw":[64],"pixels":[65],"images,":[68],"decoder":[71],"outputs":[73],"reconstruct":[74],"code.":[81],"Experimental":[82],"study":[83],"proves":[84],"proposed":[87],"learning":[89,96],"based":[90],"has":[92],"superior":[93],"performance":[94],"in":[95,109],"features":[97],"than":[98],"traditional":[99],"without":[101],"any":[102],"prior":[103],"knowledge,":[104],"presenting":[105],"good":[107],"potential":[108],"verification":[111],"vein.":[114]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}